Executive Summary | Toward AI, Machine Learning, and Natural Language Processing

There is a lot of excitement in the market about artificial intelligence (AI), machine learning (ML), and natural language processing (NLP). Although many of these technologies have been available for decades, new advancements in compute power along with new algorithmic developments are making these technologies more attractive to early adopter companies. These organizations are embracing advanced analytics technologies for a number of reasons including improving operational efficiencies, better understanding behaviors, and gaining competitive advantage.

We have found that organizations are making use of these technologies in numerous ways. Some are applying machine learning for traditional use cases such as fraud and risk analysis or analyzing customer behavior. Others are using machine learning for preventive maintenance. Still others are building interactive chatbots and B2B applications that provide intelligence using a natural language interface. Deep learning is being employed to classify images and diagnose diseases. The use cases are wide and growing.

Data scientists are leading the way in terms of model building using various technology approaches. They are making use of open source analytics technologies such as R and Python as an important part of the advanced analytics efforts. Commercial analytics products are also being deployed by many, and some use open source in conjunction with commercial platforms. Organizations are also continuing to build out their data environments for analytics, with many beginning to utilize multiplatform data architectures.

Another important trend is that more AI technology approaches are targeting users beyond data scientists (e.g., a broad range of business users and “citizen” data scientists). Analytics applications more often include built-in AI/ML algorithms that are targeted to make it easier for business analysts and users to find insights. These include natural-language-based search interfaces, automated suggestions, and automated model building.

Early adopter experience provides clues as to best practices for those getting started with these technologies to gain advantage more quickly.

Early adopter experience provides clues as to best practices for those getting started with these technologies to gain advantage more quickly. For instance, early adopters are building centers of excellence (CoEs) and are hiring data scientists and analytics leaders. They are focused on data quality for analytics, operationalizing their analytics, and providing training opportunities. Overall, one thing is clear—organizations that are utilizing these technologies now are gaining value. In fact, early adopters are much more likely to be satisfied with their analytics deployments than those that are just getting started with more advanced analytics or those that have no plans. Organizations are also seeing value as they move through the analytics success cycle.

This TDWI Best Practices Report examines organizations’ experiences with and plans for machine learning, NLP, and AI, including technology plans as well as organizational strategies. It also looks at various advanced analytics challenges and how organizations are overcoming them. It examines the importance of new open source models and automated intelligence. Finally, it offers recommendations and best practices for successfully implementing more advanced analytics such as machine learning and AI in the organization.


SAS, ThoughtSpot, Inc., and Vertica sponsored the research and writing of this report.

About the Author

Fern Halper, Ph.D., is vice president and senior director of TDWI Research for advanced analytics. She is well known in the analytics community, having been published hundreds of times on data mining and information technology over the past 20 years. Halper is also co-author of several Dummies books on cloud computing and big data. She focuses on advanced analytics, including predictive analytics, text and social media analysis, machine-learning, AI, cognitive computing and big data analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead data analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her by email (, on Twitter (, and on LinkedIn (

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